METIS: Exploring mobile phone sensing offloading for efficiently supporting social sensing applications

Abstract

Abstract-Mobile phones play a pivotal role in supporting ubiquitous and unobtrusive sensing of human activities. However, maintaining a highly accurate record of a user's behavior throughout the day imposes significant energy demands on the phone's battery. In this paper, we present the design, implementation, and evaluation of METIS: an adaptive mobile sensing platform that efficiently supports social sensing applications. The platform implements a novel sensor task distribution scheme that dynamically decides whether to perform sensing on the phone or in the infrastructure, considering the energy consumption, accuracy, and mobility patterns of the user. By comparing the sensing distribution scheme with sensing performed solely on the phone or exclusively on the fixed remote sensors, we show, through benchmarks using real traces, that the opportunistic sensing distribution achieves over 60% and 40% energy savings, respectively. This is confirmed through a real world deployment in an office environment for over a month: we developed a social application over our frameworks, that is able to infer the collaborations and meetings of the users. In this setting the system preserves over 35% more battery life over pure phone sensing.

A Survey on Mobile Social Signal Processing

“…Medusa [Ra et al 2012] achieved crowd-sensing and simultaneous coordination of multiple mobile devices. METIS [Rachuri et al 2013] is a distributed system that decides, based on the device status and user context, to perform on-device or infrastructure-oriented sensing. MSF [Cardone et al 2013] is a recent data collection framework that complies to multi-pipeline architecture and targets in providing an abstraction regarding the sensing process.…”

“…Medusa [Ra et al 2012] allows a coordinator to retrieve a certain type of sensor-data from a specific device. Furthermore, METIS [Rachuri et al 2013] is the first work that lightens a mobile device by selectively perform sensing through the infrastructure but simultaneously narrows the mobility and increases the intrusiveness of the system. MSF [Cardone et al 2013] is focussing on easing the development of sensing applications.…”

Software engineering for mobility: reflecting on the past, peering into the future

“…On the other hand, energy resources are still a critical concern, especially given today's energy-hungry devices with their large, high-definition screens and powerful processors. These energy concerns are rapidly creeping into the software engineering process [27,28].…”

“…As described previously, the persistent availability of the "cloud" has fundamentally changed mobile computing. While precursors to computational clouds have existed since the beginning of computing, the integration of this cloud with persistent connectivity has given rise to many opportunities in offloading, most commonly moving heavy computational loads from lightweight mobile devices to the cloud [45][46][47], but also in offloading sensing tasks to a persistently connected ambient infrastructure [28]. These directions have even come full circle to offloading to opportunistically available mobile partners [48], hearkening back to some of our original predictions regarding uses of mobile ad hoc networks.…”

Open source smartphone libraries for computational social science

“…Designing applications that appropriately balance between energy efficiency, data collection, storage, and transmission continues to be both a non-trivial task as well as an item on the sensor-research community's agenda [12].…”